LED control system based on power grid balancing

Abstract

The present invention relates to the field of power grid technology, and specifically to an LED control system based on power grid balancing, comprising: an LED power grid, a power grid balancing control device, an LED matrix and an LED control device; the LED matrix comprises a plurality of LEDs, each of which is connected to a different position in the LED power grid; the power grid balancing control device is configured to regard each LED in the LED power grid as a load, collect the equipment parameters of each LED, and perform balanced power distribution in the LED power grid, so that the load balance degree in the LED power grid is the highest; the LED control device is configured to perform polymorphic control on each LED in the LED matrix. The present invention realizes load balancing and fine brightness control of the LED power grid, and improves the performance, energy efficiency and reliability of the LED system.

Description

Technical Field

[0001] The present disclosure relates to, but is not limited to, the field of power grid technology, and in particular to an LED control system based on power grid balancing. Background Art

[0002] In recent years, with the rapid development of LED (light-emitting diode) technology, LED has been widely used in lighting, display and other fields. However, the design and optimization of LED control systems still face a series of challenges, one of which is how to achieve load balancing of LED power grids to ensure that each LED obtains appropriate power supply and improve overall efficiency. In order to solve these problems, this paper proposes a LED control system based on power grid balancing, which aims to achieve load balancing distribution of LED power grids, thereby improving the performance and energy efficiency of LED systems.

[0003] In the past, LED control systems often encountered problems such as load imbalance, energy waste, and system performance degradation. Traditional load balancing methods are often based on fixed power distribution strategies, ignoring the different needs of each LED in the LED power grid. In reality, the power requirements of different LEDs vary depending on their location, application scenario, and operating status. Traditional methods are unable to dynamically distribute power according to actual needs, resulting in insufficient power supply for some LEDs and energy waste for other LEDs. In addition, the existing technology lacks efficient LED control methods, and it is impossible to achieve fine control of LED brightness, which affects the visual effects and energy utilization of the LED system. Summary of the invention

[0004] The present disclosure provides an LED control system based on power grid balancing. The present invention realizes load balancing and fine brightness control of the LED power grid, and improves the performance, energy utilization efficiency and reliability of the LED system.

[0005] In order to solve the above problems, the technical solution of the present invention is achieved as follows:

[0006] The LED control system based on power grid balancing includes: an LED power grid, a power grid balancing control device, an LED matrix and an LED control device; the LED matrix includes a plurality of LEDs, each of which is connected to a different position in the LED power grid; the power grid balancing control device is configured to regard each LED in the LED power grid as a load, collect the equipment parameters of each LED, and perform balanced power distribution in the LED power grid, so that the load balance degree in the LED power grid is the highest; the LED control device is configured to perform polymorphic control on each LED in the LED matrix, specifically including: using polymorphic representation of the initial brightness of the LED, and representing the brightness information of the LED as a polymorphic |ψ LED>, let n be the number of polymorphic bits, corresponding to the number of brightness levels of the LED; by applying polymorphic gate operations, evolve the polymorphic |ψ of the LED LED >, to simulate the change process of LED brightness; initialize the particle swarm, each particle represents an evolution angle θ; the position of the particle represents the candidate value of the evolution angle, and the speed represents the search direction of the particle in the solution space; for each particle, the fitness is calculated based on the fitness function according to the difference between the LED brightness and the target brightness; find the particle with the highest fitness from the particle swarm, and take its evolution angle as the global optimal angle θ global ; Update the position and velocity of each particle to find a better solution; Apply the evolution angle θ of each particle to the polymorphic gate operation to update the polymorphic state of the LED

[0007] |ψ LED >; Based on the updated LED polymorphism |ψ LED >, control the brightness value of LED;

[0008] Furthermore, the polymorphic gate operation represented by the following formula is applied to simulate the change process of LED brightness:

[0009]

[0010] in is the i-th multi-bit Pauli-X matrix, θ is the evolution angle, and U(θ) is the evolution result.

[0011] Furthermore, the fitness function is expressed using the following formula:

[0012]

[0013] Where MSE is the mean square error between LED brightness and target brightness; Fitness is fitness.

[0014] Furthermore, the position and velocity of each particle are updated using the following formula:

[0015]

[0016] Where ω is the inertia weight, c1 and c2 are acceleration coefficients, rand1 and rand2 are random numbers between 0 and 1; Velocity new Velocity is the updated speed. old is the speed before updating; θ current Position is the current evolution angle; old Position is the position before updating. new is the updated position; θ local The optimal evolutionary perspective of history.

[0017] Furthermore, the device parameters include: the number of LEDs in the LED matrix M, the maximum power supply capacity P of each LED max ; Power requirement of each LED P demand and the initial power supply P for each LED supply .

[0018] Furthermore, the grid balancing control device regards each LED in the LED grid as a load, collects the device parameters of each LED, and performs balanced power distribution in the LED grid, so that the load balance degree in the LED grid is the highest. The method includes:

[0019] Step 1: Assume that the initial power demand change of the i-th LED is The initial power demand change rate of the i-th LED is

[0020] Step 2: Considering the power demand change rate and the historical optimal power demand change value, update the power demand change value of the LED;

[0021] Step 3: Considering the optimal power demand change value of itself and adjacent LEDs, update the power demand change rate of the LED;

[0022] Step 4: Calculate the power supply for each LED;

[0023] Step 5: Update the historical optimal power demand change value; update the optimal power demand change value of the adjacent LED;

[0024] Step 6: Correct the power demand change value of the LED; based on the corrected power demand change value; update the power demand change rate of the LED again;

[0025] Step 7: Execute steps 4 to 6 repeatedly for a set number of times, and distribute the power in the LED grid evenly based on the final calculated power supply of the LEDs, so that the load balance in the LED grid is the highest.

[0026] Furthermore, the following formula is used in step 2 to update the power demand change value of the LED:

[0027]

[0028] Where t is the time step;

[0029] In step 3, use the following formula to update the rate of change of the LED power demand:

[0030]

[0031] in, is the power demand change rate of the ith LED at time step t+1; the power demand change rate refers to the rate of change of the power demand of the LED; k is the inertia weight, which controls the influence of the current speed on the power demand change rate at the next moment; is the power demand change rate of the ith LED at time step t, that is, the power demand change rate at the previous moment; a1 is the individual experience weight, indicating the degree to which each LED is attracted by the historical best power demand change value; r1: individual random number, introducing randomness to increase the diversity of search; P best,i is the historical optimal power demand change value of the i-th LED, that is, the optimal power supply value obtained by the LED in the past iteration; is the power demand change value of the ith LED at time step t, that is, the power demand change value at the previous moment; a2 is the group experience weight, which indicates the degree to which each LED is attracted by the historical best power demand change value of the adjacent LED; r2 is the group random number, which introduces randomness to increase the diversity of the search; P best,neighbor,i is the historical optimal power demand change value of the neighboring LEDs of the i-th LED, that is, the optimal power supply value obtained by the neighboring LEDs in the past iterations; is the quantum power demand change value of the ith LED at time step t, which serves as the reference value for updating the power demand change rate.

[0032] Furthermore, the power supply for each LED is calculated using the following formula in step 4:

[0033]

[0034] Among them, P supply,i is the power supply for the i-th LED.

[0035] Furthermore, in step 5, the historical optimal power demand change value is updated using the following formula:

[0036] If P supply,i >P best,i , then P best,i =P supply,i ;

[0037] In step 5, the optimal power demand change value of the adjacent LEDs is updated using the following formula:

[0038] If P best,j >P best,neighbor,i , then P best,neighbor,i =P best,j .

[0039] Among them, P best,j is the historical optimal power demand change value of the jth adjacent LED of the ith LED.

[0040] Further, in step 6, the power demand change value of the LED is corrected using the following formula;

[0041]

[0042] Among them, P supply,j Supplying power to the jth neighboring LED of the i-th LED;

[0043] Use the following formula to update the LED power demand change rate again:

[0044]

[0045] The LED control system based on grid balancing of the present invention has the following beneficial effects: Traditional LED control systems often have the problem of load imbalance in power distribution, some LEDs are underpowered, and other LEDs waste energy. The control system of the present invention realizes balanced power distribution by treating each LED as a load and dynamically distributing power according to the power demand, supply capacity and initial power supply of each LED. This enables each LED in the LED grid to obtain appropriate power supply, thereby improving the overall energy utilization efficiency, reducing energy waste and extending the service life of the LED. The present invention introduces polymorphic representation and polymorphic gate operation to make the brightness control of the LED more refined and diversified. The initial brightness of the LED is represented by polymorphism, and the polymorphic gate operation is applied to simulate the change process of the LED brightness, thereby realizing polymorphic control of the LED brightness. This polymorphic control method can not only realize more precise brightness adjustment, but also adapt to different application scenarios and requirements, and provide richer visual effects. Combined with the particle swarm optimization algorithm, the present invention further improves the control accuracy in terms of LED brightness control. By calculating the fitness function, the particles are evolved according to the difference between the LED brightness and the target brightness, and the brightness control of the LED is optimized. The introduction of this algorithm makes the brightness control of LED more intelligent and adaptive, effectively improving the performance and visual effect of the LED system. The present invention not only maximizes the load balance in the LED power grid, but also realizes the fine control of LED brightness, thereby greatly improving the overall performance of the LED system. Load-balanced power distribution allows each LED to be satisfied without wasting energy, while fine brightness control improves the adjustability and adaptability of the LED. This not only improves the visual effect of LED applications, but also saves energy costs, reduces energy consumption, and contributes to sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of the system structure of an LED control system based on power grid balancing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present disclosure clearer and more understandable, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not used to limit the present disclosure.

[0048] Example 1: Reference Figure 1 , an LED control system based on power grid balancing, comprising: an LED power grid, a power grid balancing control device, an LED matrix and an LED control device; the LED matrix comprises a plurality of LEDs, each of which is connected to a different position in the LED power grid; the power grid balancing control device is configured to regard each LED in the LED power grid as a load, collect the device parameters of each LED, and perform balanced power distribution in the LED power grid, so that the load balance degree in the LED power grid is the highest; the LED control device is configured to perform polymorphic control on each LED in the LED matrix, specifically comprising: using polymorphic representation of the initial brightness of the LED, and representing the brightness information of the LED as a polymorphic |ψ LED >, let N be the number of polymorphic bits, corresponding to the number of brightness levels of the LED; by applying polymorphic gate operations, evolve the polymorphic |ψ of the LED LED >, to simulate the change process of LED brightness; initialize the particle swarm, each particle represents an evolution angle θ; the position of the particle represents the candidate value of the evolution angle, and the speed represents the search direction of the particle in the solution space; for each particle, the fitness is calculated based on the fitness function according to the difference between the LED brightness and the target brightness; find the particle with the highest fitness from the particle swarm, and take its evolution angle as the global optimal angle θ global ; Update the position and velocity of each particle to find a better solution; Apply the evolution angle θ of each particle to the polymorphic gate operation and update the polymorphic |ψ of the LED LED >; Based on the updated LED polymorphism |ψ LED >, control the brightness value of the LED.

[0049] Specifically, the LED grid in the system is a power supply network that contains multiple LEDs. These LEDs are organized into an LED matrix, and each LED is connected to a different location in the LED grid. This arrangement allows the LEDs to be centrally controlled to achieve overall lighting adjustment. The grid balancing control device is a key part of the innovation, which treats each LED in the LED grid as a load. By collecting the device parameters of each LED, the control device achieves balanced power distribution. This means that the system adjusts the power distribution according to the energy consumption characteristics of each LED to achieve load balance in the LED grid. This balance helps to improve energy efficiency and reduce energy waste. The LED control device introduces a polymorphic control strategy to control the brightness of the LED through quantum states. The brightness information of each LED is represented as a polymorphic quantum state |ψ LED >, where N is the number of multi-state bits, corresponding to the number of brightness levels of the LED. By applying multi-state gate operations, the quantum state of the LED |ψ LED >Can evolve, thus simulating the change process of LED brightness. This control strategy is more sophisticated and adjustable than the traditional current control method. In the LED control device, a particle swarm optimization algorithm is introduced to optimize the brightness of the LED. Each particle represents an evolution angle θ, its position represents the candidate value of the evolution angle, and the speed represents the search direction of the particle in the solution space. The fitness function is used to evaluate the fitness of each particle, and the particle with the highest fitness is found, and its evolution angle is taken as the global optimal angle θ global This method can help find the best LED brightness control solution.

[0050] The multi-state control strategy allows for highly refined adjustment of the brightness of the LED. Since the brightness level of the LED can be represented as a multi-state quantum state, continuous and smooth brightness changes can be achieved to meet different lighting needs. Compared with the traditional current control method, the multi-state control strategy can achieve higher energy efficiency. Through precise brightness adjustment, energy waste can be avoided and unnecessary energy consumption can be minimized. The multi-state control strategy is highly flexible and can be adapted to different application scenarios and lighting needs. The brightness of the LED can be adjusted in real time according to environmental changes to meet the needs of users. Due to the superiority of the multi-state control strategy, the brightness of the LED can respond quickly to the control signal. This is very important in application scenarios where the brightness needs to be adjusted quickly, such as stage lighting or emergency lighting. The particle swarm optimization algorithm is used to find the optimal solution for LED brightness control. By continuously optimizing the position and speed of the particles, the system can find the global optimal angle to achieve the best brightness match.

[0051] Embodiment 2: Based on the previous embodiment, the polymorphic gate operation represented by the following formula is applied to simulate the change process of LED brightness:

[0052]

[0053] in is the i-th multi-bit Pauli-X matrix, θ is the evolution angle, and U(θ) is the evolution result.

[0054] Specifically, is the Pauli-X matrix of the ith multibit: In quantum computing, the Pauli-X matrix is ​​a basic quantum gate used to perform bit flipping operations on qubits. For the ith multibit, Indicates that a Pauli-X gate is applied to the bit. In an LED control device, this operation corresponds to changing the brightness level of the LED, because the brightness level of the LED can be represented by the ground state of the quantum state.

[0055] θ is the evolution angle: θ represents the angle used to evolve the polymorphic gate operation. This angle affects the changes made by the polymorphic gate operation to the brightness state of the LED. By adjusting the value of θ, different brightness adjustments can be achieved.

[0056] U(θ) is the evolution result: U(θ) represents the evolution result obtained after applying the polymorphic gate operation. This result corresponds to the change in LED brightness. By using different θ values, different brightness change processes can be simulated, thereby achieving precise brightness control.

[0057] In summary, the polymorphic gate operation represented by this formula achieves analog changes in LED brightness by changing the brightness level of the LEDs in the LED matrix. By adjusting the evolution angle θ, precise brightness control can be achieved to meet different lighting needs. This operation has higher accuracy and adjustability than traditional current regulation methods.

[0058] Embodiment 3: Based on the previous embodiment, the fitness function is expressed using the following formula:

[0059]

[0060] Where MSE is the mean square error between LED brightness and target brightness; Fitness is fitness.

[0061] Specifically, MSE is the mean square error between the LED brightness and the target brightness: Mean Squared Error (MSE) is a statistical measure used to measure the difference between the actual value and the predicted value. In LED control, MSE is used to measure the difference between the actual LED brightness and the target brightness. It is expressed as:

[0062]

[0063] LED brightness i is the actual brightness of the ith LED, targetrightness i is the target brightness of the ith LED.

[0064] Fitness is a measure of the quality of an individual in an evolutionary algorithm. Here, the fitness function uses the inverse of the MSE, which is scaled and transformed by an exponential function. The fitness function is designed to make the fitness inversely proportional to the MSE, that is, the fitness increases as the MSE decreases. Using the form of an exponential function can ensure that the increase in fitness is nonlinear. The fitness function calculates the fitness based on the mean square error (MSE) between the actual brightness of the LED and the target brightness. The fitness value increases as the MSE decreases. Using the form of an exponential function makes the change in the fitness value more significant, which helps the optimization algorithm find the best solution faster.

[0065] Example 4: Based on the previous example, the position and velocity of each particle are updated using the following formula:

[0066]

[0067] Where ω is the inertia weight, c1 and c2 are acceleration coefficients, rand1 and rand2 are random numbers between 0 and 1; Velocity new Velocity is the updated speed. old is the speed before updating; θ current Position is the current evolution angle; old Position is the position before updating. new is the updated position; θ local The optimal evolutionary perspective of history.

[0068] Specifically, ω is the inertia weight: The inertia weight ω is used to balance the influence between the particle's historical motion direction and the global optimal position. Its value is usually between 0 and 1 and is used to adjust the particle's inertia. A larger ω can make the particle more inclined to maintain its historical motion direction, while a smaller ω will make the particle more inclined to move toward the global optimal position.

[0069] c1 and c2 are acceleration coefficients: The acceleration coefficients c1 and c2 represent the degree to which the particle is attracted by the individual optimal position and the global optimal position, respectively. They are used to adjust the degree to which the particle is affected by these two gravitational forces. A larger c1 will increase the attraction of the particle to the individual optimal position, while a larger c2 will increase the attraction of the particle to the global optimal position.

[0070] rand1 and rand2 are random numbers between 0 and 1: rand 1 and rand 2 are random numbers uniformly distributed between 0 and 1, used to introduce randomness. They are multiplied by the difference between θlocal and θglobal to affect the velocity adjustment of the particle.

[0071] θ local is the optimal evolution angle of history: θ local represents the best evolution angle in the particle’s own history, that is, the best solution obtained in the previous iteration.

[0072] θ global is the global optimal evolution angle: θ global It represents the global optimal evolution angle in the entire particle swarm, that is, the evolution angle of the particle with the highest fitness among all particles in the particle swarm.

[0073] In summary, this formula describes how the particle position and velocity are updated in the particle swarm optimization algorithm. By considering the inertia weight, acceleration coefficient, random number, and gravity of the individual and global optimal positions, the particles can search for a better solution in the solution space. By continuously updating the position and velocity, the particles gradually tend to the global optimal solution, thereby optimizing the brightness control in the LED control device.

[0074] Embodiment 5: Based on the previous embodiment, the device parameters include: the number of LEDs in the LED matrix M, the maximum power supply capacity of each LED P max ; Power requirement of each LED P demand and the initial power supply P for each LED supply .

[0075] Specifically, the number of LEDs M: This parameter indicates the number of LEDs in the LED matrix. The number of LEDs in the LED matrix determines the scale and capacity of the entire lighting system. In grid balancing control, the number of LEDs will affect the calculation of power distribution and load balancing.

[0076] The maximum power supply capacity P of each LED max : This parameter indicates the maximum power that each LED can provide. It reflects the power supply limit of the LED, that is, the maximum power that the LED can withstand. In the power balance distribution, considering the maximum power supply capacity of the LED can ensure that the LED does not exceed its tolerance range.

[0077] Power requirement per LED P demand : This parameter represents the power demand of each LED, that is, the power required by the LED to achieve its target brightness. The power demand is related to the brightness and performance of the LED. A greater brightness usually requires more power supply.

[0078] Initial power supply P for each LED supply :This parameter indicates the initial power supply of the LED. The initial power supply may be affected by the initial configuration of the system, it can be a preset value or derived from other control strategies. In grid balancing control, the initial power supply will affect the starting point of load distribution.

[0079] These device parameters are used in the grid balancing control device to calculate the power distribution of LEDs to achieve load balancing and efficient energy utilization. By monitoring the power supply and demand of LEDs, the system can dynamically adjust the power distribution according to actual conditions to ensure balanced load distribution in the LED grid, thereby optimizing the performance of the LED lighting system.

[0080] Embodiment 6: Based on the previous embodiment, the grid balancing control device regards each LED in the LED grid as a load, collects the device parameters of each LED, and performs balanced power distribution in the LED grid. The method for achieving the highest load balance in the LED grid includes:

[0081] Step 1: Assume that the initial power demand change of the i-th LED is The initial power demand change rate of the i-th LED is The specific process is as follows: For each LEDi: Initialize the initial power demand change value The initial power demand change value can be set according to application requirements or historical data. Initialize the initial power demand change rate It can also be set based on application requirements or historical data. The purpose of initializing the power demand change value and change rate is to provide a starting point for the subsequent steps of power balancing control. These initial values ​​will affect the subsequent calculation and adjustment process. The initial power demand change value can reflect the initial state of the system, while the initial power demand change rate may be affected by the expected change rate of the system or historical data. The purpose of step 1 is to provide initial conditions for the power balancing control process. The initial power demand change value and change rate are used as initial information, which can be corrected through calculation and adjustment in subsequent iterations. They provide a starting point for the system state and help the system gradually move towards a load balancing state.

[0082] Step 2: Consider the power demand change rate and the historical optimal power demand change value, and update the power demand change value of the LED; the specific process is as follows: For each LEDi: Use the current power demand change rate and the historical optimal power demand change value Calculate the new power demand change value X ′i . Some update rules can be adopted, for example: Where α is the trade-off coefficient. The update of the power demand change value realizes the gradual adjustment of the power demand by considering the historical optimal value and the preset change rate. This process aims to gradually make the power demand change value tend to balance to better meet the power distribution requirements of the system. The role of step 2 is to guide the power demand change value of the LED to adjust towards the equilibrium state. By combining the historical optimal value and the change rate, the system can gradually approach the load balance state and avoid drastic changes and shocks. This step helps to control the stability and convergence of the system.

[0083] Step 3: Consider the optimal power demand change value of itself and adjacent LEDs, and update the power demand change rate of the LED; the specific process is as follows: For each LEDi: Get its own optimal power demand change value Get the optimal power demand change value of adjacent LEDs This can be an average of neighboring LEDs or some other related method. Use the current power demand change rate As well as the optimal power demand change value of itself and adjacent LEDs, calculate the new power demand change rate V′ i . Some update rules can be adopted, for example: Where β is the trade-off coefficient. The purpose of updating the power demand change rate is to achieve better load balancing between adjacent LEDs. By introducing the information of adjacent LEDs, the system can adjust its own power demand change rate according to the change trend of adjacent LEDs to achieve a more balanced power distribution. The purpose of step 3 is to adjust the power demand change rate of the LED based on the information of the adjacent LEDs, so as to achieve a more balanced load distribution between adjacent LEDs. This helps to avoid overuse of certain LEDs in power distribution, resulting in an unbalanced situation.

[0084] Step 4: Calculate the power supply for each LED;

[0085] The specific process is as follows: For each LEDi: Use the updated power demand change value X′ i and the power demand change rate V′ i , and the initial power supply P supply , calculate the LED power supply P i Some calculation formulas can be used, such as: P i =P supply +X′ i ·V′ i. The calculation of power supply is based on the updated power demand change value and change rate, as well as the initial power supply. With this information, the actual power supply obtained by each LED can be calculated to meet the change in its power demand. The role of step 4 is to convert the change in power demand and the rate of change into the actual power supply. Through this calculation, the system can determine how much power each LED can actually get, so as to carry out subsequent balanced power distribution. In short, step 4 calculates the impact of the power demand change value and change rate, and converts it into actual power supply to meet the power demand of the LED. This helps to more accurately control the power supply of the LED during the power distribution process.

[0086] Step 5: Update the historical optimal power demand change value; update the optimal power demand change value of the adjacent LED; the purpose of updating the historical optimal value and the optimal value of the adjacent LED is to guide the adjustment direction of the system in subsequent iterations. By saving the historical optimal value, the system can use past good performance to guide the calculation; by updating the optimal value of the adjacent LED, the system can make more accurate adjustments based on the information of the adjacent LED. The role of step 5 is to provide guidance and guidance so that the system gradually approaches the load balance state in subsequent iterations. The preservation of the historical optimal value can prevent the system from jumping into an unstable state, and the update of the optimal value of the adjacent LED helps to achieve better load distribution between adjacent LEDs.

[0087] Step 6: Correct the power demand change value of the LED; based on the corrected power demand change value; update the power demand change rate of the LED again; according to the actual power supply situation, correct the power demand change value of the LED, and gradually adjust the system by updating the power demand change rate. This process is designed to gradually approach the power balance state of the system while making corrections according to the actual situation. The role of step 6 is to make corrections according to the actual situation to more accurately adjust the power demand change value and change rate of the LED. By making corrections according to the power supply situation, the system can better meet the actual load distribution needs and avoid over-allocation or under-allocation. In short, step 6 gradually achieves a more accurate load balancing control effect by correcting the power demand change value according to the actual situation and updating the power demand change rate again according to the corrected value. This helps to optimize the performance and energy utilization of the LED power grid.

[0088] Step 7: Loop through steps 4 to 6 for a set number of times, and based on the final calculated LED power supply, distribute the power in the LED grid evenly, so that the load balance in the LED grid is the highest. The purpose of step 7 is to gradually bring the system closer to a power balance state through multiple iterations. Each iteration will adjust the power demand according to the actual power supply situation to achieve a balanced load distribution. Through cyclic iterations, the system can gradually optimize the performance and energy utilization of the LED grid. In short, step 7 executes steps 4 to 6 through multiple iterations to achieve a balanced power distribution in the LED grid. This process aims to gradually adjust the power demand so that the load in the LED grid gradually tends to be balanced and optimize the performance of the entire system.

[0089] Embodiment 7: Based on the previous embodiment, the following formula is used in step 2 to update the power demand change value of the LED:

[0090]

[0091] Where t is the time step;

[0092] In step 3, use the following formula to update the rate of change of the power demand of the LED:

[0093]

[0094] in, is the power demand change rate of the ith LED at time step t+1; the power demand change rate refers to the rate of change of the power demand of the LED; k is the inertia weight, which controls the influence of the current speed on the power demand change rate at the next moment; is the power demand change rate of the ith LED at time step t, that is, the power demand change rate at the previous moment; a1 is the individual experience weight, indicating the degree to which each LED is attracted by the historical best power demand change value; r1: individual random number, introducing randomness to increase the diversity of search; P best,i is the historical optimal power demand change value of the i-th LED, that is, the optimal power supply value obtained by the LED in the past iteration; is the power demand change value of the ith LED at time step t, that is, the power demand change value at the previous moment; a2 is the group experience weight, which indicates the degree to which each LED is attracted by the historical best power demand change value of the adjacent LED; r2 is the group random number, which introduces randomness to increase the diversity of the search; P best,neighbor,i is the historical optimal power demand change value of the neighboring LEDs of the i-th LED, that is, the optimal power supply value obtained by the neighboring LEDs in the past iterations; is the quantum power demand change value of the ith LED at time step t, which serves as the reference value for updating the power demand change rate.

[0095] Specifically, in this formula, represents the rate of change of power demand of the ith LED at the next time step t+1, represents the rate of change of power demand of the ith LED at the current time step t, k is the inertia weight, a1 and a2 are experience weights, r1 and r2 are random numbers, P best,i is the historical optimal power demand change value of the i-th LED, P best,neighbor,i is the historical optimal power demand change value of the adjacent LEDs, is the power demand change value of the i-th LED at the current time step t. The principle of this formula is to update the power demand change rate according to multiple factors to achieve more refined load balancing control. Among them, the inertia weight k controls the degree of maintenance of the current change rate, the individual and group experience weights a1 and a2 respectively represent the influence of the historical optimal values ​​of the individual and group on the current change rate, and the random numbers r1 and r2 introduce randomness to increase the diversity of the search. By combining these factors, the formula implements a strategy for adjusting the power demand change rate according to historical and current conditions. The formula in step 3 achieves a more accurate adjustment of the power demand change rate by considering the experience, randomness and current trends of individuals and groups. This helps the system approach the load balancing state faster, avoid falling into the local optimal solution, and improve the globality of the search. In summary, the two formulas in Example 7 are used to update the power demand change value and change rate in steps 2 and 3 respectively, and a more refined and effective LED power grid load balancing control strategy is achieved by comprehensively considering historical, individual and group information.

[0096] Embodiment 8: Based on the previous embodiment, the power supply of each LED is calculated using the following formula in step 4:

[0097]

[0098] Among them, P supply,i is the power supply for the i-th LED.

[0099] Specifically, the power supply of each LED in the next time step is calculated by multiplying the ratio of the power demand change value of each LED to the total demand change value by the maximum power supply capacity. This calculation method ensures that the power supply of each LED is proportional to its proportion in the load. The formula in step 4 is used to calculate the proportion of each LED in the load based on the power demand change value of each LED, and apply this proportion to the maximum power supply capacity to obtain the power supply of each LED in the next time step. This calculation process ensures load balancing so that each LED obtains appropriate power supply to meet its needs.

[0100] Embodiment 9: Based on the previous embodiment, the following formula is used in step 5 to update the historical optimal power demand change value:

[0101] If P supply,i >P best,i , then P best,i =P supply,i ;

[0102] In step 5, the optimal power demand change value of the adjacent LEDs is updated using the following formula:

[0103] If P best,j >P best,neighbor,i , then P best,neighbor,i =P best,j .

[0104] Among them, P best,j is the historical optimal power demand change value of the jth adjacent LED of the ith LED.

[0105] Update the historical optimal power demand change value of the LED and the historical optimal power demand change value of the adjacent LED for use in subsequent iterations. If the power supply of a certain LED is greater than its historical optimal power demand change value, it means that this power supply is a better choice, so the historical optimal value needs to be updated. The historical optimal values ​​of adjacent LEDs are also updated similarly to keep the information synchronized and transmitted. The two formulas in step 5 are used to update the historical optimal power demand change value of the LED and the historical optimal power demand change value of the adjacent LED. These updates ensure that the historical optimal value of each LED is always the best power supply value it has obtained. At the same time, through the update of the optimal values ​​of adjacent LEDs, information is transmitted between LEDs, which helps to better guide the system to find the global optimal solution.

[0106] Embodiment 10: Based on the previous embodiment, in step 6, the following formula is used to correct the power demand change value of the LED;

[0107]

[0108] Among them, P supply,j Supplying power to the jth neighboring LED of the i-th LED;

[0109] Use the following formula to update the LED power demand change rate again:

[0110]

[0111] Specifically, in these formulas, represents the change in power demand of the i-th LED at the next time step t+1, P demand represents the power requirement of the ith LED, Psupply,j represents the power supply of the jth neighboring LED of the i-th LED, represents the rate of change of power demand of the ith LED at the next time step t+1, represents the rate of change of power demand of the i-th LED at the current time step t, and k, a1, r1, a2, and r2 are the weights and random numbers explained before. The formula in step 6 aims to make it consistent with the expected power demand by correcting the power demand change value. First, the formula uses the ratio between power demand and power supply to correct the power demand change value so that it matches the power supply while meeting the demand. Then, the power demand change rate is updated again using the historical optimal values ​​of individuals and groups and other weights to guide the search process towards a better solution. These formulas in step 6 realize the correction and re-update of the power demand change value and change rate. By correcting the power demand change value, it is possible to ensure that the power demand of the LED is consistent with its needs, and further optimization is performed on the basis of load balancing. Updating the power demand change rate again helps to better guide the search process and improve the convergence and efficiency of the algorithm.

[0112] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0113] The preferred embodiments of the present disclosure are described above with reference to the accompanying drawings, but the scope of the present disclosure is not limited thereby. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the present disclosure shall be within the scope of the present disclosure.

Claims

1. The LED control system based on power grid balancing is characterized by: include: LED power grid, power grid balancing control device, LED matrix and LED control device; the LED matrix includes a plurality of LEDs, each LED is connected to a different position in the LED power grid; the power grid balancing control device is configured to regard each LED in the LED power grid as a load, collect device parameters of each LED, and perform balanced power distribution in the LED power grid, so that the load balance degree in the LED power grid is the highest; The LED control device is configured to perform polymorphic control on each LED in the LED matrix, specifically comprising: using polymorphic representation of the initial brightness of the LED, representing the brightness information of the LED as a polymorphic representation ,set up is the number of polymorphic bits, corresponding to the number of brightness levels of the LED; by applying polymorphic gate operations, the polymorphic state of the LED is evolved , to simulate the change process of LED brightness; initialize the particle swarm, each particle represents an evolution angle ; The position of the particle represents the candidate value of the evolution angle, and the speed represents the search direction of the particle in the solution space; For each particle, the fitness is calculated based on the fitness function according to the difference between the actual brightness of the LED and the target brightness; Find the particle with the highest fitness from the particle group and take its evolution angle as the global optimal angle ; Update the position and velocity of each particle to find a better solution; The evolution angle of each particle Applied to the polymorphic door operation, update the polymorphic state of the LED ; Based on the updated LED polymorphism , control the brightness value of LED; Apply the polymorphic gate operation expressed by the following formula to simulate the change process of LED brightness: ； in It is A multi-bit Pauli-X matrix, From an evolutionary perspective, As a result of evolution; The device parameters include: the number of LEDs in the LED matrix , Maximum power supply capacity of each LED ;Power requirements per LED and the initial power supply for each LED ; The grid balancing control device regards each LED in the LED grid as a load, collects the device parameters of each LED, and performs balanced power distribution in the LED grid, so that the load balance degree in the LED grid is the highest. The method includes: Step 1: Set The initial power demand change of each LED is ;No. The initial power demand change rate of each LED is ; Step 2: Considering the power demand change rate and the historical optimal power demand change value, update the power demand change value of the LED; Step 3: Considering the optimal power demand change value of itself and adjacent LEDs, update the power demand change rate of the LED; Step 4: Calculate the power supply for each LED; Step 5: Update the historical optimal power demand change value; update the optimal power demand change value of the adjacent LED; Step 6: Correct the power demand change value of the LED; based on the corrected power demand change value; update the power demand change rate of the LED again; Step 7: cyclically execute steps 4 to 6 for a set number of times, and based on the final calculated power supply of the LEDs, distribute the power in the LED power grid evenly, so that the load balance in the LED power grid is the highest.

2. The LED control system based on power grid balancing according to claim 1, characterized in that: The fitness function is expressed using the following formula: ； Where MSE is the mean square error between the actual brightness of the LED and the target brightness; For fitness.

3. The LED control system based on power grid balancing according to claim 2, characterized in that: Update the position and velocity of each particle using the following formula: ； in is the inertia weight, and is the acceleration factor, and is a random number between 0 and 1; is the updated speed, is the speed before the update; is the current evolution perspective; is the position before the update, is the updated position; It is the optimal evolutionary perspective of history.

4. The LED control system based on power grid balancing according to claim 3, characterized in that: In step 2, use the following formula to update the LED power demand change value: ； in, is the time step; In step 3, use the following formula to update the rate of change of the LED power demand: ； in, For the LEDs in time step The power demand change rate refers to the rate of change of the power demand of the LED; is the inertia weight, which controls the influence of the current speed on the rate of change of power demand at the next moment; For the LEDs in time step The rate of change of electricity demand is the rate of change of electricity demand at the previous moment; is the individual experience weight, which indicates the degree to which each LED is attracted by the historical best power demand change value; : Individual random numbers introduce randomness to increase the diversity of the search; For the The historical optimal power demand change value of an LED, that is, the best power supply value obtained by the LED in the past iteration; For the LEDs in time step The change value of electricity demand is the change value of electricity demand at the previous moment; is the group experience weight, which indicates the degree to which each LED is attracted by the historical optimal power demand change value of the adjacent LEDs; It introduces randomness into the group random number to increase the diversity of the search; For the The historical optimal power demand change value of the adjacent LEDs of the LED, that is, the optimal power supply value obtained by the adjacent LEDs in the past iterations; For the LEDs in time step The quantum power demand change value is used as a reference value for updating the power demand change rate.

5. The LED control system based on power grid balancing according to claim 4, characterized in that: In step 4, use the following formula to calculate the power supply for each LED: ； in, For the Power supply for each LED.

6. The LED control system based on power grid balancing according to claim 5, characterized in that: In step 5, the following formula is used to update the historical optimal power demand change value: if ,but ; In step 5, the optimal power demand change value of the adjacent LEDs is updated using the following formula: if ,but ; in, For the The first LED The historical optimal power demand change value of each adjacent LED.

7. The LED control system based on power grid balancing according to claim 6, characterized in that: In step 6, the power demand change value of the LED is corrected using the following formula; ； in, For the The first LED Power supply for adjacent LEDs; Use the following formula to update the LED power demand change rate again: 。

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